def register_model(model_uri, name):
"""
Create a new model version in model registry for the model files specified by ``model_uri``.
Note that this method assumes the model registry backend URI is the same as that of the
tracking backend.
:param model_uri: URI referring to the MLmodel directory. Use a ``runs:/`` URI if you want to
record the run ID with the model in model registry. ``models:/`` URIs are
currently not supported.
:param name: Name of the registered model under which to create a new model version. If a
registered model with the given name does not exist, it will be created
automatically.
:return: Single :py:class:`mlflow.entities.model_registry.ModelVersion` object created by
backend.
"""
client = MlflowClient()
try:
client.create_registered_model(name)
except MlflowException as e:
if e.error_code == ErrorCode.Name(RESOURCE_ALREADY_EXISTS):
eprint(
"Registered model %s already exists. Using it to create a new version."
% name)
else:
raise e
if RunsArtifactRepository.is_runs_uri(model_uri):
source = RunsArtifactRepository.get_underlying_uri(model_uri)
(run_id, _) = RunsArtifactRepository.parse_runs_uri(model_uri)
return client.create_model_version(name, source, run_id)
else:
return client.create_model_version(name, source=model_uri, run_id=None)
def register_model(model_uri,
name,
await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS):
"""
Create a new model version in model registry for the model files specified by ``model_uri``.
Note that this method assumes the model registry backend URI is the same as that of the
tracking backend.
:param model_uri: URI referring to the MLmodel directory. Use a ``runs:/`` URI if you want to
record the run ID with the model in model registry. ``models:/`` URIs are
currently not supported.
:param name: Name of the registered model under which to create a new model version. If a
registered model with the given name does not exist, it will be created
automatically.
:param await_registration_for: Number of seconds to wait for the model version to finish
being created and is in ``READY`` status. By default, the function
waits for five minutes. Specify 0 or None to skip waiting.
:return: Single :py:class:`mlflow.entities.model_registry.ModelVersion` object created by
backend.
"""
client = MlflowClient()
try:
create_model_response = client.create_registered_model(name)
eprint("Successfully registered model '%s'." %
create_model_response.name)
except MlflowException as e:
if e.error_code == ErrorCode.Name(RESOURCE_ALREADY_EXISTS):
eprint(
"Registered model '%s' already exists. Creating a new version of this model..."
% name)
else:
raise e
if RunsArtifactRepository.is_runs_uri(model_uri):
source = RunsArtifactRepository.get_underlying_uri(model_uri)
(run_id, _) = RunsArtifactRepository.parse_runs_uri(model_uri)
create_version_response = client.create_model_version(
name, source, run_id)
else:
create_version_response = client.create_model_version(
name,
source=model_uri,
run_id=None,
await_creation_for=await_registration_for)
eprint("Created version '{version}' of model '{model_name}'.".format(
version=create_version_response.version,
model_name=create_version_response.name))
return create_version_response
def register_model(model_uri,
name,
await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS):
"""
Create a new model version in model registry for the model files specified by ``model_uri``.
Note that this method assumes the model registry backend URI is the same as that of the
tracking backend.
:param model_uri: URI referring to the MLmodel directory. Use a ``runs:/`` URI if you want to
record the run ID with the model in model registry. ``models:/`` URIs are
currently not supported.
:param name: Name of the registered model under which to create a new model version. If a
registered model with the given name does not exist, it will be created
automatically.
:param await_registration_for: Number of seconds to wait for the model version to finish
being created and is in ``READY`` status. By default, the function
waits for five minutes. Specify 0 or None to skip waiting.
:return: Single :py:class:`mlflow.entities.model_registry.ModelVersion` object created by
backend.
.. code-block:: python
:caption: Example
import mlflow.sklearn
from sklearn.ensemble import RandomForestRegressor
mlflow.set_tracking_uri("sqlite:////tmp/mlruns.db")
params = {"n_estimators": 3, "random_state": 42}
# Log MLflow entities
with mlflow.start_run() as run:
rfr = RandomForestRegressor(**params).fit([[0, 1]], [1])
mlflow.log_params(params)
mlflow.sklearn.log_model(rfr, artifact_path="sklearn-model")
model_uri = "runs:/{}/sklearn-model".format(run.info.run_id)
mv = mlflow.register_model(model_uri, "RandomForestRegressionModel")
print("Name: {}".format(mv.name))
print("Version: {}".format(mv.version))
.. code-block:: text
:caption: Output
Name: RandomForestRegressionModel
Version: 1
"""
client = MlflowClient()
try:
create_model_response = client.create_registered_model(name)
eprint("Successfully registered model '%s'." %
create_model_response.name)
except MlflowException as e:
if e.error_code == ErrorCode.Name(RESOURCE_ALREADY_EXISTS):
eprint(
"Registered model '%s' already exists. Creating a new version of this model..."
% name)
else:
raise e
if RunsArtifactRepository.is_runs_uri(model_uri):
source = RunsArtifactRepository.get_underlying_uri(model_uri)
(run_id, _) = RunsArtifactRepository.parse_runs_uri(model_uri)
create_version_response = client.create_model_version(
name, source, run_id, await_creation_for=await_registration_for)
else:
create_version_response = client.create_model_version(
name,
source=model_uri,
run_id=None,
await_creation_for=await_registration_for)
eprint("Created version '{version}' of model '{model_name}'.".format(
version=create_version_response.version,
model_name=create_version_response.name))
return create_version_response
def test_parse_runs_uri_invalid_input(uri):
with pytest.raises(MlflowException):
RunsArtifactRepository.parse_runs_uri(uri)
def test_parse_runs_uri_valid_input(uri, expected_run_id,
expected_artifact_path):
(run_id, artifact_path) = RunsArtifactRepository.parse_runs_uri(uri)
assert run_id == expected_run_id
assert artifact_path == expected_artifact_path
def test_parse_runs_uri_invalid_input(uri):
with pytest.raises(MlflowException, match="Not a proper runs"):
RunsArtifactRepository.parse_runs_uri(uri)
